A Fortune 500 Retailer engaged Focal Point to assist with improving the inventory management and demand planning performance of the company. The Retailer had recently experienced a significant merger, increasing the internal pressure to reduce excess inventory, streamline the process for purchasing of new inventory, and more efficiently manage the flow of existing inventory across the supply chain. To help address these challenges, the Focal Point team was introduced and tasked with building a forecasting model that would outperform the existing platform.
After the success of that project, the Focal Point Data Analytics team was quickly integrated into other initiatives to reduce excess inventory and improve demand forecasting through advanced data analytics.
Project Summary
Challenge 1: Improve Forecast Accuracy
Improving forecast accuracy and demand-planning performances was a top priority in order for the Retailer to remain at the top of their industry. Focal Point sought to identify ways the Retailer could leverage the functionalities native to their current demand-planning software and improve forecast accuracy. In addition, the Focal Point team was challenged with testing the accuracy of the Multi-Linear Regression forecasting algorithm that the Retailer had relied on previously, comparing the algorithm against five alternatives. Using back-casted forecast data and historical sales, Focal Point simulated each algorithm and identified the optimal mix for the Retailer.
After the implementation of the suggested algorithms, the Retailer experienced a 2%-3% increase in total forecast accuracy, which is expected to increase over time when adjusting for seasonality. Since only about 25% of the multi-algorithm model was applied to the total unique SKU population, these results were a huge success for the Retailer.
Challenge 2: Understand and Reduce Excess Inventory
The Retailer sought methods for improving visibility into excess inventory levels and accurately measuring its impact. The Retailer also needed assistance with developing a roadmap to reduce excess inventory. In order to implement a plan more successfully than prior attempts by the Retailer, Focal Point developed a multi-year Excess Reduction Program, which incorporated over 30 distinct projects and initiatives.
Initiatives covering the cross-functional organization were scoped for reduction opportunity, organizational impact, and overlap among initiatives. Focal Point played a key role in:
- Conducting interviews with leadership in the business and in IT
- Identifying data sources across many databases and data stores
- Developing reports to scope and measure initiative impact
- Creating dashboards for monitoring
- Working with omni-channel cross-functional organization to implement action plans
Key staff members of the Retailer were then able to more easily view inventory levels and threats to corporate goals, which resulted in lower inventory levels and improved turns and GMROI.
Challenge 3: Efficiently Reallocate Inventory
Due to the recent merger, the Retailer wanted a better procedure for facilitating the redistribution of excess inventory across a broader network of stores and distribution centers. Focal Point collaborated with the Retailer to develop an end-to-end solution to support the truck-building and transfer processes.
The transfer model worked to automatically:
- Identify and prioritize the inventory stored at the closing distribution centers
- Match that inventory to ideal store / distribution center locations
- Submit the request to a truck-building algorithm to maximize the efficiency of the inventory transfer (by loading full trucks to minimal locations and within the closest proximity)
- Examine dimensional data, such as inventory-cube size and weight, standard pack, case pack and pallet quantity, to determine the optimal arrangement of inventory to be shipped, based on the available truck space
This new solution resulted in significant cost savings and operational efficiencies.
Challenge 4: Enable Standardized Reporting
Standardized reporting for forecast accuracy, as well as consistent methods for calculating forecast performance, did not exist across the enterprise of the Retailer. In addition, the Retailer did not have a central data repository for the forecast or the sales data necessary to run a reliable forecast accuracy calculation. As a result of inconsistent data storage, management, and reporting, leadership at the Retailer was unable to obtain the insights needed to effectively manage their forecast performance.
To solve this problem, Focal Point developed a process for aggregating the forecast and sales data in a central repository, from which the company could automate forecast reporting. The Focal Point team then developed custom reporting that offered both weighted forecast accuracy by sales volume and by cost of goods sold, which allowed the Retailer to look at accuracy in terms of units vs. dollars. The Retailer could then drill into what SKUs, departments, and stores were performing well and identify those that needed improvement. Additional ad hoc reporting options gave management the ability to quickly pull accurate reports that allowed for better decision making, as well.
